Machine and equipment assets are engineered to perform particular tasks as part of a business process. For example, assets can include, among other things and without limitation, industrial manufacturing equipment on a production line, drilling equipment for use in mining operations, wind turbines that generate electricity on a wind farm, transportation vehicles, and the like. As another example, assets may include devices that aid in diagnosing patients such as imaging devices (e.g., X-ray or MRI systems), monitoring equipment, and the like. The design and implementation of these assets often takes into account both the physics of the task at hand, as well as the environment in which such assets are configured to operate.
Low-level software and hardware-based controllers have long been used to drive machine and equipment assets. However, the rise of inexpensive cloud computing, increasing sensor capabilities, and decreasing sensor costs, as well as the proliferation of mobile technologies, have created opportunities for creating novel industrial and healthcare based assets with improved sensing technology and which are capable of transmitting data that can then be distributed throughout a network. As a consequence, there are new opportunities to enhance the business value of some assets through the use of novel industrial-focused hardware and software.
Sensor are widely used in industrial settings to monitor the condition of associated machinery and operations thereof. It may be advantageous to provide the ability to perform descriptive, diagnostic, and predictive data analytics and/or other such operations on the sensors. Often, however, machine faults are not detected until a complete failure of one or more devices occurs. Previous methods of detecting damage to assets could not detect intermittent damage or determine the extent of the damage.
It may be desirable to detect damage to assets when it first occurs and be able to immediately identify the damaged asset. It may also be desirable to measure the performance of an asset in situ without removing the asset from operation. Also, as technology develops, it may be desirable to emulate one or more hardware devices in software. One such situation arises when hardware and/or software becomes outdated (referred to as legacy hardware or code), but the older hardware is needed to perform the analytics.
In the conventional way of troubleshooting an asset when damage is known to have occurred, it is necessary to attach probes and extensions to the asset (e.g., locomotive, wind turbine, etc.). Typically, fault words are stored in a log and a user would need to approach the device and plug in to read the log, which does not provide analytics information. This is cumbersome, time consuming, and labor intensive. User visibility and insight into the status of the asset using conventional troubleshooting methods is also limited.